Big Data-Driven Assessment of Proposals to Improve Enterprise Flexibility Through Control Options Untested in Practice

The study discusses how the process information reserves can be used for improving production flexibility on the basis of production component variation control. Building upon the assumption that the data in question describe the functions of the production components as those experience perturbations, we submit that it may be possible to predict the productivity and economic efficiency for new, previously unused control options. We elaborate on the production components that ensure production flexibility, approaching them as a highly autonomous holon. We determine the conditions that make it possible to analyze the holon control options in isolation from controlling all other components. We also suggest a solution to the problem of predicting the employees’ impact on production. Our suggestion is based on limiting the types of impact accessible to a human operator. The study looks at the reasons behind the uncertainty of production control simulation modeling results, which stem from the peculiarities of the data collected. We propose a criterion for assessing the impact of uncertainty on the productivity indicators’ variation. This study offers a systemic overview of the aspects of a production simulation aimed at assessing the impact of uncertainty on productivity predictions. As an example, we review how uncertainty affects the variation of coke consumption and productivity of a blast furnace, which is ensured by selecting a suitable option for iron ore sinter quality control.